Predictive Systems: Living with Imperfect Predictors
The standard regression approach to modeling return predictability seems too restrictive in one way but too lax in another. A predictive regression models expected returns as an exact linear function of a given set of predictors but does not exploit the likely economic property that innovations in expected returns are negatively correlated with unexpected returns. We develop an alternative framework - a predictive system - that accommodates imperfect predictors and beliefs about that negative correlation. In this framework, the predictive ability of imperfect predictors is supplemented by information in lagged returns as well as lags of the predictors. Compared to predictive regressions, predictive systems deliver different and substantially more precise estimates of expected returns as well as different assessments of a given predictor's usefulness.
Graduate School of Business, University of Chicago, NBER, and CEPR (Pastor) and the Wharton School, University of Pennsylvania, and NBER (Stambaugh). Helpful comments were received from seminar participants at the Fall 2006 NBER Asset Pricing Meeting, the Wharton Frontiers of Investing conference, Boston College, Hong Kong University of Science and Technology, National University of Singapore, Singapore Management University, University of Chicago, University of Iowa, University of Michigan, University of Pennsylvania, University of Texas at Austin, and University of Texas at Dallas. We also thank Ken French, Pietro Veronesi, and especially Jonathan Lewellen (NBER discussant) and John Cochrane for many helpful suggestions. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
Lubos Pástor & Robert F. Stambaugh, 2009. "Predictive Systems: Living with Imperfect Predictors," Journal of Finance, American Finance Association, vol. 64(4), pages 1583-1628, 08. citation courtesy of